Polypyrrole-Coated Capillary Coupled to HPLC for In-Tube Solid-Phase Microextraction and Analysis of Aromatic Compounds in Aqueous Samples
Bibliographic record
Abstract
In-tube solid-phase microextraction (SPME) based on a polypyrrole (PPY)-coated capillary was investigated for the extraction of aromatic compounds from aqueous solutions. The PPY-coated capillary was coupled on-line to HPLC that was programmed with an autosampler to achieve automated in-tube SPME and HPLC analysis. Three groups of aromatics, including both polar and nonpolar compounds, were examined. The results demonstrated that the PPY coating had a higher extraction efficiency than the currently used commercial capillary coatings, especially for polycyclic aromatic compounds and polar aromatics due to the increasing pi-pi interactions, interactions by polar functional groups, and hydrophobic interactions between the polymer and the analytes. In addition to the functional groups in the PPY coating, which contributed to the higher extraction efficiency and selectivity toward analytes, the coating's porous surface structure,which was revealed by electron microscopy experiments, provided a high surface area that allowed for high extraction efficiency. It was found that the extraction efficiency and selectivity could be tuned by changing the coating thickness. The preliminary study of the extraction mechanism indicated that analytes were extracted onto the PPY coating mainly by an adsorption mechanism. The method was used for the extraction and analysis of both polar and nonpolar aromatics in aqueous samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".